[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

A cellular automata based approach to track salient objects in videos

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

In this paper we present an algorithm to track the motion of a salient object using Cellular Automata (CA). The overall work, taking inspiration from recent research on insect sensory motor system, investigates the application of non conventional computer vision approaches to evaluate their effectiveness in fulfilling this task. The proposed system employs the Sobel operator to individual frames, performing further elaborations through a CA, with the aim of detecting and characterizing moving entities within the field of view to support collision avoidance from the perspective of the viewer. The paper formally describes the adopted approach as well as its experimentation videos representing plausible situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://www.youtube.com/watch?v=HDb9StNG8_Q.

  2. https://docs.scipy.org/doc/scipy/reference/ndimage.html.

  3. https://opencv.org/.

  4. https://www.youtube.com/watch?v=SW3rvS3wLqg from which we digitally removed the “Ball” text.

References

  • Ando N, Kanzaki R (2015) A simple behaviour provides accuracy and flexibility in odour plume tracking—the robotic control of sensory-motor coupling in silkmoths. J Exp Biol 218(23):3845–3854

    Article  Google Scholar 

  • Ando N, Kanzaki R (2017) Using insects to drive mobile robots–hybrid robots bridge the gap between biological and artificial systems. Arthropod Struct Dev 46(5):723–735

    Article  Google Scholar 

  • Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072

    Article  Google Scholar 

  • Bandini S, Crociani L, Vizzari G (2017) An approach for managing heterogeneous speed profiles in cellular automata pedestrian models. J Cell Autom 12(5):401–421

    MathSciNet  Google Scholar 

  • Canny J (1987) A computational approach to edge detection. In: Fischler MA, Firschein O (eds) Readings in computer vision. Morgan Kaufmann, San Francisco, CA, pp 184–203

    Google Scholar 

  • Carrieri A, Crociani L, Vizzari G, Bandini S (2018) Motion detection and characterization in videos with cellular automata. In: Cellular automata—13th international conference on cellular automata for research and industry, ACRI 2018 lecture notes in computer science, vol 11115. Springer, pp 102–111

  • Chan RW, Gabbiani F (2013) Collision-avoidance behaviors of minimally restrained flying locusts to looming stimuli. J Exp Biol 216(4):641–655

    Article  Google Scholar 

  • Chang CL, Zhang YJ, Gdong YY (2004) Cellular automata for edge detection of images. In: Proceedings of 2004 international conference on machine learning and cybernetics (IEEE Cat. No. 04EX826), vol. 6. IEEE, pp. 3830–3834

  • Chopard B (2012) Cellular automata modeling of physical systems. Springer, New York, pp 407–433

    Google Scholar 

  • Deriche R (1987) Optimal edge detection using recursive filtering. Int J Comput Vis 2:167–187

    Article  Google Scholar 

  • Fotowat H, Gabbiani F (2011) Collision detection as a model for sensory-motor integration. Ann Rev Neurosci 34(1):1–19

    Article  Google Scholar 

  • Georgoudas I, Kyriakos P, Sirakoulis G, Andreadis I (2010) An fpga implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields. Microprocess Microsyst 34(7):285–300

    Article  Google Scholar 

  • Guo J, Ren T, Huang L, Liu X, Cheng MM, Wu G (2017) Video salient object detection via cross-frame cellular automata. In: 2017 IEEE international conference on multimedia and expo (ICME). IEEE, pp 325–330

  • Hartbauer M (2017) Simplified bionic solutions: a simple bio-inspired vehicle collision detection system. Bioinspir Biomim 12(2):026007

    Article  Google Scholar 

  • Ioannidis K, Andreadis I, Sirakoulis GC (2012) An edge preserving image resizing method based on cellular automata. In: Sirakoulis GC, Bandini S (eds) Cellular automata. Springer, Berlin, pp 375–384

    Chapter  Google Scholar 

  • Kalogeropoulos G, Sirakoulis GC, Karafyllidis I (2013) Cellular automata on FPGA for real-time urban traffic signals control. J Supercomput 65(2):664–681

    Article  Google Scholar 

  • Katiyar S, Arun P (2014) Comparative analysis of common edge detection techniques in context of object extraction. arXiv preprint arXiv:1405.6132

  • Kumar T, Sahoo G (2010) A novel method of edge detection using cellular automata. Int J Comput Appl 9(4):38–44

    Google Scholar 

  • Popovici A, Popovici D (2002) Cellular automata in image processing. In: Fifteenth international symposium on mathematical theory of networks and systems. Citeseer

  • Prewitt JM (1970) Object enhancement and extraction. Picture Process Psychopictorics 10(1):15–19

    Google Scholar 

  • Qin Y, Lu H, Xu Y, Wang H (2015) Saliency detection via cellular automata. In: 2015 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 110–119

  • Roberts LG (1963) Machine perception of three-dimensional solids. Ph.D. thesis, Massachusetts Institute of Technology

  • Rundo L, Militello C, Russo G, Pisciotta P, Valastro LM, Sabini MG, Vitabile S, Gilardi MC, Mauri G (2016) Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model. In: Cellular Automata—12th international conference on cellular automata for research and industry, ACRI 2016. Lecture notes in computer science, vol 9863. Springer, pp 323–333

  • Santé I, García AM, Miranda D, Crecente R (2010) Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc Urban Plan 96(2):108–122

    Article  Google Scholar 

  • Sobel I (1990) An isotropic 3 × 3 image gradient operator. In: Freeman H (ed) Machine vision for three-dimensional scenes. Academic Press, San Diego, CA, pp 376–379

    Google Scholar 

  • Toffoli T, Margolus N (1987) Cellular automata machines: a new environment for modeling. MIT Press, Cambridge

    Book  Google Scholar 

  • Vergassola M, Villermaux E, Shraiman BI (2007) ‘Infotaxis’ as a strategy for searching without gradients. Nature 445:406

    Article  Google Scholar 

  • Voges N, Chaffiol A, Lucas P, Martinez D (2014) Reactive searching and infotaxis in odor source localization. PLoS Comput Biol 10(10):1–13

    Article  Google Scholar 

  • Wolfram S (1984) Cellular automata as models of complexity. Nature 311(5985):419–424

    Article  Google Scholar 

  • Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv (CSUR) 38(4):13

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giuseppe Vizzari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Crociani, L., Vizzari, G., Carrieri, A. et al. A cellular automata based approach to track salient objects in videos. Nat Comput 18, 865–873 (2019). https://doi.org/10.1007/s11047-019-09766-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-019-09766-2

Keywords

Navigation